How can i get a language model to respect causal details in lab notes?

[Emily_M]

I’ve been trying to use a large language model to help categorize and generate hypotheses from my messy lab notes, but I’m hitting a wall where its suggestions feel generic and miss the specific physical constraints of my experimental setup. Has anyone else found that the model’s statistical pattern recognition just doesn’t grasp the underlying causal mechanisms you’re working with?

[PaulXW]

Yep, I'm in that same loop. The model latches onto statistical patterns and skips the hard physics in my notes. I tried feeding in explicit constraints and real units, but the suggestions still felt generic, like they’d apply to any lab with a similar vibe rather than our setup.

[Luke.C]

I ran a tiny test: pasted a few pages with the actual constants, a rough schematic, and a hard constraint about allowable temperature ramps. It still spit out hypotheses that would fit almost any experiment, not the bottleneck we actually face. It did show me where the model was guessing, though.

[Grace78]

Do you think the root issue is data quality and labeling, or is the model really missing the causal structure and just pattern matching?

[Natalie55]

I’ve tried letting it propose a dozen near misses, then I prune hard and test one. Often it ignores the physical limits and I end up discarding the idea anyway. It’s a rough compass, not a map.